The use of renewable energy to reduce the effects of climate change and global warming has become an increasing trend. In order to improve the prediction ability of renewable energy, various prediction techniques have been developed. The aims of this review are illustrated as follows. First, this survey attempts to provide a review and analysis of machine-learning models in renewable-energy predictions. Secondly, this study depicts procedures, including data pre-processing techniques, parameter selection algorithms, and prediction performance measurements, used in machine-learning models for renewable-energy predictions. Thirdly, the analysis of sources of renewable energy, values of the mean absolute percentage error, and values of the coefficient of determination were conducted. Finally, some possible potential opportunities for future work were provided at end of this survey.

A Survey of Machine Learning Models in Renewable Energy Predictions

This study presents a broad view of the current state of the art of ML applications in the manufacturing sectors that have a considerable impact on sustainability and the environment, namely renewable energies (solar, wind, hydropower, and biomass), smart grids, the industry of catalysis and power storage and distribution. Artificial neural networks are the most preferred techniques over other ML algorithms because of their generalization capabilities. Demands for ML techniques in the energy sectors will increase considerably in the coming years, since there is a growing demand of academic programmes related to artificial intelligence in science, math, and engineering. Data generation, management, and safety are expected to play a key role for the successful implementation of ML algorithms that can be shared by major stakeholders in the energy sector, thereby promoting the development of ambitious energy management projects. New algorithms for producing reliable data and the addition of other sources of information (e.g., novel sensors) will enhance flow of information between ML and systems. It is expected that unsupervised and reinforcement learning will take a central role in the energy sector, but this will depend on the expansion of other major fields in data science such as big data analytics. Massive implementations, specialized algorithms, and new technologies like 5G will promote the development of sustainable applications of ML in non-industrial applications for energy management.

Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage

• SLR is conducted using PRISMA approach and148 articles are selected and critically analyzed. • The results show the extent of digital technologies adoption in agriculture. • The potential benefits of digital technologies and roadblocks hindering their implementation in agriculture sector are identified and discussed. • The study will positively impact the research around agriculture 4.0. Agriculture is considered one of the most important sectors that play a strategic role in ensuring food security. However, with the increasing world's population, agri-food demands are growing — posing the need to switch from traditional agricultural methods to smart agriculture practices, also known as agriculture 4.0. To fully benefit from the potential of agriculture 4.0, it is significant to understand and address the problems and challenges associated with it. This study, therefore, aims to contribute to the development of agriculture 4.0 by investigating the emerging trends of digital technologies in the agricultural industry. For this purpose, a systematic literature review based on Protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses is conducted to analyse the scientific literature related to crop farming published in the last decade. After applying the protocol, 148 papers were selected and the extent of digital technologies adoption in agriculture was examined in the context of service type, technology readiness level, and farm type. The results have shown that digital technologies such as autonomous robotic systems, internet of things, and machine learning are significantly explored and open-air farms are frequently considered in research studies (69%), contrary to indoor farms (31%). Moreover, it is observed that most use cases are still in the prototypical phase. Finally, potential roadblocks to the digitization of the agriculture sector were identified and classified at technical and socio-economic levels. This comprehensive review results in providing useful information on the current status of digital technologies in agriculture along with prospective future opportunities. 

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The digitization of agricultural industry – a systematic literature review on agriculture 4.0

A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem

In this article we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change, and it can contribute to combatting the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI’s greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based, and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combatting climate change, while reducing its impact on the environment.

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The AI Gambit — Leveraging Artificial Intelligence to Combat Climate Change: Opportunities, Challenges, and Recommendations

Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia

The aim of this study is to accurately forecast the changes in water level of a reservoir located in Malaysia with two different scenarios; Scenario 1 (SC1) includes rainfall and water level as input and Scenario 2 (SC2) includes rainfall, water level, and sent out. Different time horizons (one day ahead to seven days) will be investigated to check the accuracy of the proposed models. In this study, four supervised machine learning algorithms for both scenarios were proposed such as Boosted Decision Tree Regression (BDTR), Decision Forest Regression (DFR), Bayesian Linear Regression (BLR) and Neural Network Regression (NNR). Eighty percent of the total data were used for training the datasets while 20% for the dataset used for testing. The models’ performance is evaluated using five statistical indexes; the Correlation Coefficient (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), and Relative Squared Error (RSE). The findings showed that among the four proposed models, the BLR model outperformed other models with R2 0.998952 (1-day ahead) for SC1 and BDTR for SC2 with R2 0.99992 (1-day ahead). With regards to the uncertainty analysis, 95PPU and d-factors were adopted to measure the uncertainties of the best models (BLR and BDTR). The results showed the value of 95PPU for both models in both scenarios (SC1 and SC2) fall into the range between 80% to 100%. As for the d-factor, all values in SC1 and SC2 fall below one.

https://www.mdpi.com/2071-1050/12/15/6121/pdf

Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy

Sediment is a universal issue that is generated in the river catchment and affects the river flow, reservoir capacity, hydropower generation and dam structure. This paper aims to present the result of experimentation in sediment load estimation using various machine learning algorithms as a powerful AI approach. The data was collected from eight locations in upstream area of Ringlet reservoir catchment. The input variables are discharge and suspended solid. It was found that there is strong correlation between sediment and suspended solid with correlation coefficient of R = 0.9. The developed ML model successfully estimated the sediment load with competitive results from ANN, Decision Tree, AdaBoost and SVM. The best result was produced by SVM (v-SVM version) where very low RMSE was generated for both training and testing dataset despite its more complicated hyperparameters setup. The results also show a promising application of machine learning for future prediction in hydro-informatic systems.

http://www.jeeng.net/pdf-137847-66755?filename=A Performance Comparison.pdf

A Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systems

Evapotranspiration is a significant variable of a watershed hydrological and water quality processes for surface and subsurface modelling in a basin scale. Most of the calibration and validation hydrological parameters rely on streamflow discharge. Inaccuracy calculation of the PET could disturb all the simulation processes. Hence, this study aimed to evaluate the SWAT capability on the estimation of potential evapotranspiration in Kelantan characterised as forest-dominated topography in upstream catchment. The model calibration applied for the data set from 1985 to 2000 by utilising the SUFI-2 algorithm and validated for the period from 2001 to 2016 with three different PET methods: P-M, P-T, and HG. The model indicated that there was no significant difference in the value of NSE and R2. The calibration simulations give very good result in term of NSE and R2 with HG achieved 0.85, 0.86, P-T achieved 0.84, 0.86 and P-M achieved 0.83 and 0.85 respectively. However, HG tends to give lower simulated flow than P-T and P-M in terms of PBIAS were 2.5%, −5.9% and −1.9% respectively. While during validation, all three PETs indicated simulated higher than the observed with PBIAS were −23.3%, −29.3% and −28.5%. The P-M method tends to estimate lower PET than HG and P-T with yearly simulated was accounted for approximately 66%, 70%, and 71% respectively of annual precipitation. Overall, all the available options PETs methods in SWAT, give good performance model after calibration and validation.

Khairul Faizal Kushiar

Papers

Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia

Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy

A Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systems

Potential Evapotranspiration Estimation Methods for Water Balance Analysis Using SWAT: A Case Study of Kelantan River Basin, Kelantan